Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Irwin, G. (2008Abstract: The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting.The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade -Lucus -Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

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The assessment of well-designed road-lighting systems is necessary since their performance can be critically reduced by incorrect installation. During the operational life of a system, it is also necessary to assess the effects of deterioration in the luminaire fittings, changes in road surface or surroundings and changing user needs. An automated vehicle-mounted system constitutes the most practical technical solution to carry out this task. Previous research produced image acquisition and analysis systems for measuring luminance and uniformity levels of road lighting (Glenn 2000). This paper builds on this work and describes the methods employed to assess the combined parameters of luminance, illuminance and glare.A description of the system components is given, including the CCD digital video cameras, which are mounted on the test vehicle. The cameras are pre-calibrated to estimate relationships between gray value of light images and lighting parameters (luminance and illuminance). Appropriate infra-red and neutral density filters are employed to control the wavelength and limit the light entering into the cameras.Differential GPS, 3D orientation sensors, and image flow analysis, are employed to accurately estimate the position of the vehicle. Automated image analysis methods are further developed to speed up the position and image analysis process.Multidirectional measurements of light output are achieved using multiple journeys and multiple cameras on the same road-segment, which provide data on different observation lines. Interpolation techniques are employed to estimate the complete profile and produce isolux contours. Results produced so far indicate that lighting A Zatari: